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Adaptive educational hypermedia is one of the first and most popular kinds of adaptive hypermedia. It applies adaptive hypermedia to the domain of education. Example systems of adaptive educational hypermedia are ELM-ART〔 〕 by Gerhard Weber et al., InterBook〔 〕 by Peter Brusilovsky et al., Personal Reader by Nicola Henze et al., and AHA!〔 〕 by Paul de Bra et al. In contrast to traditional e-learning/electronic learning (and face-to-face education) systems, whereby all learners are offered or even directed a standard series of hyperlinks, adaptive educational hypermedia tailors what the learner sees to that learner's goals, abilities, needs, interests, and knowledge of the subject, by providing hyperlinks that are most relevant to the user. Essentially, the teaching tools "adapt" to the learner. Of course, this requires the system to be able to effectively infer the learner's needs and desires. Many fields of research including human-computer interaction, educational technology, cognitive science, intelligent tutoring systems and computer engineering are contributing to the development of adaptive hypermedia. Unlike intelligent tutoring systems, however, adaptive educational hypermedia does not target stand-alone systems, but hypermedia systems. Moreover, the use of adaptive hypermedia is not limited to formal (or informal) education or training endeavours. Such systems can, e.g., increase profits by adapting to consumers' searches (sometimes unconscious) for goods, services, and experiences. Thus, systems like Amazon are also examples of adaptive hypermedia, recommending books based on user preferences and prior history. Other application fields of adaptive hypermedia, beside of adaptive e-learning and adaptive e-commerce applications can be adaptive e-government applications. Generally speaking, adaptive hypermedia systems can be useful anywhere where hypertext and hypermedia is used. The most popular adaptive hypermedia systems are web-based systems. An interesting aspect of adaptive hypermedia is that it makes distinction between adaptation (system-driven personalization and modifications) and adaptability (user-driven personalization and modifications). One way of looking at it is that adaptation is automatic, whereas adaptability is not. From an epistemic point of view, adaptation can be described as analytic, a-priori, whereas adaptability is synthetic, a-posteriori. In other words, any adaptable system, as it 'contains' a human, is by default 'intelligent', whereas an adaptive system that presents 'intelligence' is more surprising and thus more interesting. This conforms with the general preference of the adaptive hypermedia research community, which considers adaptation more interesting. However, the truth of adaptive hypermedia systems is somewhere in the middle, combining and balancing adaptation and adaptability. == Framework == A common method or framework of design for an adaptive educational system is with four inter-dependent components. *Domain Model - The domain model contains several concepts that stand as the backbone for the content of the system. Each concept has a set of topics. Topics represent individual pieces of knowledge for each domain and the size of each topic varies in relation to the particular domain. Additionally, topics are linked to each other forming a semantic network. This network is the structure of the knowledge domain.〔Triantafillou, E, Pomportsis, A, & Demetriadis, S. (2003). The design and the formative evaluation of an adaptive educational system based on cognitive styles. Computers & Education , 41(87-103), Retrieved from http://www.cdli.ca/~bmann/0_ARTICLES/FormEval_Triantafillou_03.pdf 〕 *Student Model - The student model consists of a personal profile (which includes static data, e.g., name and password), cognitive profile (adaptable data such as preferences), and a student knowledge profile.〔〔 *Content model - The content model describes educational content (information pages, examples, problems, etc.) in terms of Domain Model concepts. The simplest content model relates every content item to exactly one domain concept (in this model, this concept is frequently referred to as domain topic. More advanced content models used multi-concept indexing for each content item and sometimes uses roles to express the nature of item-concept relationship.〔 *Adaptation Module - The adaptation module displays information to the user based on his or her cognitive preferences. For instance, the module will divide a page's content into chunks with conditions set to only display to certain users or preparing two variants of a single concept page with a similar condition.〔 The system architecture utilizes the three models in tandem. The conditional elements of these modules use simple variables representing how much to weight certain content(i.e. The likelihood of a piece of content showing up) depending on the user's preferences and history. Through use of the user model, a user can see basic concepts without any restriction. After seeing the basic concepts, a variable updates and allows the user to view advanced concepts in relation to the concept they have just seen. Additionally, as the user peruses more content, the system keeps track of the variables to build and adjust a knowledge level variable. As the user is deemed knowledgeable enough by the conditions, links and concepts considered too easy will be removed to streamline and simplify the experience for the user. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「adaptive educational hypermedia」の詳細全文を読む スポンサード リンク
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